Market analysis · built with machine learning and AI

See the market
in context.

Understand changing market regimes, spot emerging trends, and put your analysis on a stronger statistical footing.

Every trading strategy has a native environment where it works well. Momentum strategies perform well in trending, low-volatility regimes. Mean reversion works in choppy, range-bound markets. The question isn't whether momentum is working right now. The question is: what regime are we in, and is this regime one where momentum tends to work?

Explore supported markets Discover the approach
Market regimeUp-trend · Down-trend · Side-way
Trend forecastingDaily · Weekly · Monthly
Expected volatilityHigh · Medium · Low
Coverage overview

Supported markets

Research framework

Explore the assets in scope for market-regime analysis.

Selected instrument Nasdaq 100 · NDX
Technology-led US large-cap index

Coverage examples for research purposes. No live market data is shown.

One framework. Multiple markets. Changing regimes.

01 / The workflow

From market data
to clearer decisions.

A structured research process helps turn complex price behavior into signals you can inspect, compare, and test.

STEP 01

Observe the market

Bring together historical price and market data across the assets you follow. Compare multiple time horizons in one consistent view.

STEP 02

Identify the regime

Statistical and machine-learning methods help distinguish persistent trends, transitions, and lower-directional market phases.

STEP 03

Test what you find

Evaluate signals against historical data, review model behavior, and use context to inform your own independent research.

02 / Research toolkit

Methods built for
market complexity.

A set of complementary techniques — each with a distinct role in examining market structure, signals, and strategy hypotheses.

01

Hidden Markov Models

Estimate unobserved market states from observed price behavior and examine how regimes change over time.

REGIME DETECTION
02

K-means clustering

Group assets or market observations by similar characteristics to explore relationships and diversification hypotheses.

ASSET DISCOVERY
03

Principal Component Analysis

Reduce correlated indicators into a smaller set of components to reveal common patterns in market data.

DIMENSION REDUCTION
04

XGBoost

Model nonlinear relationships between features and outcomes for structured, testable forecasting research.

TREND FORECASTING
05

Proximal Policy Optimization

Explore reinforcement-learning approaches to strategy research in a simulated environment.

REINFORCEMENT LEARNING
06

Historical backtesting

Assess strategy hypotheses on historical data, with attention to assumptions, risk, and out-of-sample behavior.

MODEL EVALUATION

Models support research — they do not predict outcomes with certainty or provide investment advice.

03 / The technology

Used tools for
practical research.

A Python-centered stack brings data preparation, statistical analysis, machine learning, and deployment together.

PyPythonResearch & services
NNumPyNumerical computing
PdpandasData analysis
TPyTorchDeep learning
Sscikit-learnMachine learning
FFastAPIAPI services
DDockerApplication delivery